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Distributed Graph Learning Library
Dhulipala, Laxman, et al. "Compressing Graphs and Indexes with Recursive Graph Bisection." arXiv preprint arXiv:1602.08820 (2016).
sustainable computing
agent-network
Local models support for Microsoft's graphrag using ollama (llama3, mistral, gemma2 phi3)- LLM & Embedding extraction
A modular graph-based Retrieval-Augmented Generation (RAG) system
Repositories
94sustainable computing
agent-network
Distributed Graph Learning Library
Local models support for Microsoft's graphrag using ollama (llama3, mistral, gemma2 phi3)- LLM & Embedding extraction
A modular graph-based Retrieval-Augmented Generation (RAG) system
GraphRAG using Local LLMs - Features robust API and multiple apps for Indexing/Prompt Tuning/Query/Chat/Visualizing/Etc. This is meant to be the ultimate GraphRAG/KG local LLM app.
A Java DSL for the Cypher Query Language and an optional Query DSL mode
No description provided.
Repository for the NaLLM project
LLM-based ontological extraction tools, including SPIRES
Context-aware knowledge-graph based chatbot using GPT4 and Neo4j
No description provided.
The Unofficial TikTok API Wrapper In Python
GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management
No description provided.
No description provided.
A novel approach for disease subtyping
Multi-view Robust Graph-based clustering for Cancer Subtype Identification
Cancer subtype identification by consensus guided graph autoencoder
Machine Learning for Medical Data Analysis
Dhulipala, Laxman, et al. "Compressing Graphs and Indexes with Recursive Graph Bisection." arXiv preprint arXiv:1602.08820 (2016).
Implementation of the algorithms described in "Ulrike von Luxburg, A Tutorial on Spectral Clustering"
No description provided.
GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.
Scalable Graph Mining
Predict Cryptocurrency Price with Deep Learning
Graph convolutional neural network for multirelational link prediction
PGCN: Disease gene prioritization by disease and gene embedding through GCN
Cheat Sheets
No description provided.